Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter

Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never systematically been investigated before. Here, we study 9M tweets related to stocks of the 5 main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice - referred to as cashtag piggybacking - perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Among the findings of our study is that as much as 71% of the authors of suspicious financial tweets are classified as bots by a state-of-the-art spambot detection algorithm. Furthermore, 37% of them were suspended by Twitter a few months after our investigation. Our results call for the adoption of spam and bot detection techniques in all studies and applications that exploit user-generated content for predicting the stock market

The Anatomy of Conspirators: Unveiling Traits using a Comprehensive Twitter Dataset

The discourse around conspiracy theories is currently thriving amidst the rampant misinformation prevalent in online environments. Research in this field has been focused on detecting conspiracy theories on social media, often relying on limited datasets. In this study, we present a novel methodology for constructing a Twitter dataset that encompasses accounts engaged in conspiracy-related activities throughout the year 2022. Our approach centers on data collection that is independent of specific conspiracy theories and information operations. Additionally, our dataset includes a control group comprising randomly selected users who can be fairly compared to the individuals involved in conspiracy activities. This comprehensive collection effort yielded a total of 15K accounts and 37M tweets extracted from their timelines. We conduct a comparative analysis of the two groups across three dimensions: topics, profiles, and behavioral characteristics. The results indicate that conspiracy and control users exhibit similarity in terms of their profile metadata characteristics. However, they diverge significantly in terms of behavior and activity, particularly regarding the discussed topics, the terminology used, and their stance on trending subjects. Interestingly, there is no significant disparity in the presence of bot users between the two groups, suggesting that conspiracy and automation are orthogonal concepts. Finally, we develop a classifier to identify conspiracy users using 93 features, some of which are commonly employed in literature for troll identification. The results demonstrate a high accuracy level (with an average F1 score of 0.98%), enabling us to uncover the most discriminative features associated with conspiracy-related accounts

Coordinated Behavior on Social Media in 2019 UK General Election

Coordinated online behaviors are an essential part of information and influence operations, as they allow a more effective disinformation's spread. Most studies on coordinated behaviors involved manual investigations, and the few existing computational approaches make bold assumptions or oversimplify the problem to make it tractable. Here, we propose a new network-based framework for uncovering and studying coordinated behaviors on social media. Our research extends existing systems and goes beyond limiting binary classifications of coordinated and uncoordinated behaviors. It allows to expose different coordination patterns and to estimate the degree of coordination that characterizes diverse communities. We apply our framework to a dataset collected during the 2019 UK General Election, detecting and characterizing coordinated communities that participated in the electoral debate. Our work conveys both theoretical and practical implications and provides more nuanced and fine-grained results for studying online information manipulation.Comment: Version accepted in Proc. AAAI Intl. Conference on Web and Social Media (ICWSM) 2021. Added dataset DO

Social media e finanza: studio dei cashtags su Twitter per l’individuazione di spam e pattern di utilizzo anomali

Analisi dei pattern di uso dei cashtags su Twitter a partire da un dataset di tweets contententi cashtags raccolti tramite Twitter API nell'arco di tre mesi e arricchiti con informazioni provenienti da Google Finance. Lo studio, basato sull’analisi di serie storiche, ha permesso di individuare anomalie nell’utilizzo dei cashtags mirate ad accrescere la visibilità di alcune aziende poco importanti. I risultati hanno consentito l'individuazione di account bot creati con lo scopo di compiere tali azioni. Lo studio evidenzia i potenziali rischi a cui sono esposti i sistemi di trading automatici che utilizzano informazioni provenienti dal web e dai social media per fare operazioni o predire i prezzi delle azioni sui principali mercati finanziari

Holistic Approaches to Investigating, Characterizing, and Detecting Online Disinformation and Misbehavior

The contribution of the thesis is to propose new solutions and approaches for the detection of online information manipulations. Information manipulation strategies can take the forms of: (i) disinformation, which is manipulation in the form of online deceiving content, and (ii) misbehavior, which is manipulation in the form of online deceiving accounts. We investigate and address such issues as interconnected, comprehensive, and holistic problems, going beyond current literature limitations of siloed approaches. In particular, we provide technical approaches for the detection of online information manipulation capable of taking into consideration diverse strategies, multiple targets, multiple online platforms, and coordination and manipulation definitions beyond the binary concept

$FAKE: Evidence of Spam and Bot Activity in Stock Microblogs on Twitter

Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never systematically been investigated before. Here, we study 9M tweets related to stocks of the 5 main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Our results call for the adoption of spam and bot detection techniques in all studies and applications that exploit user-generated content for predicting the stock market

Sentinel Lymph Node Biopsy in Breast Cancer: Indications, Contraindications, and Controversies

Axillary lymph node status, a major prognostic factor in early-stage breast cancer, provides information important for individualized surgical treatment. Because imaging techniques have limited sensitivity to detect metastasis in axillary lymph nodes, the axilla must be explored surgically. The histology of all resected nodes at the time of axillary lymph node dissection (ALND) has traditionally been regarded as the most accurate method for assessing metastatic spread of disease to the locoregional lymph nodes. However, ALND may result in lymphedema, nerve injury, shoulder dysfunction, and other short-term and long-term complications limiting functionality and reducing quality of life. Sentinel lymph node biopsy (SLNB) is a less invasive method of assessing nodal involvement. The concept of SLNB is based on the notion that tumors drain in an orderly manner through the lymphatic system. Therefore, the SLN is the first to be affected by metastasis if the tumor has spread, and a tumor-free SLN makes it highly unlikely for other nodes to be affected. Sentinel lymph node biopsy has become the standard of care for primary treatment of early breast cancer and has replaced ALND to stage clinically node-negative patients, thus reducing ALND-associated morbidity. More than 20 years after its introduction, there are still aspects concerning SLNB and ALND that are currently debated. Moreover, SLNB remains an unstandardized procedure surrounded by many unresolved controversies concerning the technique itself. In this article, we review the main indications, contraindications, and controversies of SLNB in breast cancer in the light of the most recent publications